council

Multi-model consensus is achieved by querying frontier AI models via OpenRouter simultaneously and synthesizing their responses to mitigate individual model biases. This approach triangulates answers to highlight areas of agreement and disagreement.

Multi-model synthesis reduces single-model bias by obtaining simultaneous answers from diverse frontier models like Claude Opus, GPT-5.4, Gemini Pro, and Grok. It consolidates these diverse AI perspectives into a single coherent output, balancing blindspots.

You can use codebase context to inform multi-model AI queries through optional context gathering. This enriches the query framing by gathering relevant context from your documentation before querying the frontier models.

You need httpx to query frontier AI models via OpenRouter for multi-model consensus. The httpx dependency handles the required API communication to fetch and synthesize responses from the different models.

The best way to compare answers from multiple LLMs is using a multi-model consensus approach that queries different models and synthesizes their responses. This highlights areas of agreement and disagreement for tough decisions.

You should not use multi-model consensus for simple decisions that do not require triangulated answers from multiple frontier AI models. It is designed for complex decision-making scenarios where mitigating single-model bias is critical.